Marcin Treder

For 15 years, I was trusted to lead Meta's most important teams and projects. I didn’t expect my career to end when I took a stand against a racist video game, but it’s true. When I was handed the opportunity to lead the company through the expansion of Horizon, I was thrilled. However, I was horrified to walk into a room of men wallpapering over rampant racism, a product riddled with performance issues, and violations of public policy. Worst of all, the victims were predominantly children. When I raised the problem, I became the problem – a pattern that silences women everywhere, every day. I’ve been sexually assaulted by a boss on a business trip. I’ve been denied promotions because acknowledging my success meant acknowledging men’s failures. I’ve been told to act “less smart,” I’ve been retaliated against for doing my job. Some of you are shocked because you had no idea these things happened – some of you are shocked because you had no idea we could be honest about it. My privilege means I have the responsibility to speak up, but the discrimination and hostile work environment I experienced happens at every level, in every industry. It’s exponentially worse for women of color. It creates bad business outcomes that disproportionately harm those we should be most eager to protect, it widens the wealth gap, it puts lives at risk, and it’s against the law. It cost me my career, it almost cost my life. I’d been crotch grabbed, screamed at, told to have sex with my boss for a promotion. I survived all of it. Nothing broke me like a job where I had to tell powerful men "no." When tech companies push marginalized leaders out, they build dangerous products. This isn’t just an ethical problem. It’s a governance failure. It puts employees, shareholders, and users everywhere - especially those most in need of protection - at risk. When Meta's leadership dismissed and excluded me, they weren't just sidelining women, they were prioritizing power over people. They were putting their growth before the good. This cycle repeats itself across the industry: Women, minorities, neurodivergent people, raise ethical red flags about product safety They face retaliation and exclusion for speaking truth to power Harm then falls disproportionately on the most vulnerable users In the near-term, tech companies must strengthen protections for ethical whistleblowers, make promotion practices transparent, recommit to diversity programs and attach their success to executive compensation, and hardwire compliance into the product development process. Where Mark Zuckerberg rants about companies needing to be more masculine, where he actively dismantles his DEI teams, and where he smokescreens safeguards, my case demonstrates something inarguable: toxic and discriminatory environments aren’t just wrong, they’re anti-innovation. Hating women hurts everyone. The truth doesn’t matter to Mark Zuckerberg, but it matters to me.

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Today, we announced the open sourcing of our AI tools for Online safety at Roblox. We invest significantly in this area in building the tools and running it on our platform spanning Core, Edge, and Cloud to enhance safety for our player community. The need for improving Digital safety in the world of AI is substantial for everyone, and this initiative marks a significant step in that direction that everyone can benefit from. #AISafety #Roblox #DigitalSafety https://lnkd.in/g_bftZCb

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Here’s a truth that often gets missed: becoming an AI-first company at the enterprise level is still really hard. It requires making big investments in R&D and specialized talent and betting on still-evolving frameworks and tools — and having the commitment to push the field forward yourself where the right tools or integrations don’t yet exist for your specific use case. As deploying AI and agents becomes table stakes in more industries, some companies end up hyping prototypes or easy use cases on earnings rather than building complex, self-improving AI systems to move the needle on productivity and profit. The task of separating these AI talkers from AI doers in public markets  – and leveraging AI to do it – is why my longtime partner John Hughes and I created the Swift AI Index as a fun side project last year. We just updated the index with five new stocks as part of a quarterly refresh; check it out: https://lnkd.in/eNBGjQzK

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While we haven't officially launched, can't help but be excited to see this great artcle in the @WSJ featuring LGND AI, Inc.(http://lgnd.io) and the opportunity for AI applied to earth observation data for all sorts of important uses. This is a huge and important data source that is vastly under represented in the current AI platform shift - and we aim to change that. Big stuff ahead! https://lnkd.in/ghd76mBB

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Mark Zuckerberg and Satya Nadella at #LlamaCon discussing the pace of innovation in AI: 📈 Moore’s Law Accelerated: AI progress isn't linear. Multiple parallel innovations compound, pushing capability growth far beyond traditional expectations. 🧩 Innovation is Multi-layered: Hardware advances aren't enough. Significant improvements come from optimisations across software layers, including kernels, inference, model architectures, and prompt caching. ⚡10X Improvements Every 6-12 Months: Rapid cycles of innovation are leading to dramatic capability leaps and declining costs, fueling rapid adoption. 🔀 Shift to Multi-Model Orchestration: AI applications are evolving from single-model reliance to sophisticated orchestration of multiple specialised models and deterministic workflows. 🌐 Open Standards & Open Source Drive Adoption: Protocol standardisation (MCP, A2A) and open-source contributions accelerate enterprise adoption, enabling faster experimentation and flexibility. #LlamaCon2025 #genAI AI at Meta Meta Microsoft

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What does 'commoditization' of LLMs mean for founders and builders? Why is YKK so successful if zippers are a commodity? Will Sam open up OpenAI model weights? What did Gaurav Sukhatme have to say about history books? They actually all answer the same basic question - what's next? I think we see 3 kinds of companies emerge: 1. core private model + product wrapper - OpenAI 2. core public model (free, strategic gift) - Meta 3. commodity model + product wrapper - Perplexity Dragonfruit AI is squarely focused on #3. The reason we see Perplexity and Glean get big so quickly is because they're building on the second easiest data type - text. But that's also where incumbents and new entrants can quickly catch up, before these companies lock up their markets. Over time, I suspect the moat will shift to companies that can deal with data types with other intrinsic complexities that AI can't simplify - images, then video, then real-time video. That's what Dragonfruit is focused on. (btw, the easiest data type is numbers; wall street already did quick work on that decades ago)

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It was a lot of fun to speak last night to the current cohort from the Generation AI program, run by Amir Nissen at Boab. Last night's session was on metrics for early stage companies, and I came online just in time to hear Amir teaching the AARRR framework. That thing is timeless. I shared a few lessons from the early days of my startup career, as well as stories about fundraising in Australia. No matter where you go or what the topic is, all conversations with startup founders eventually turn to fundraising. I might have to start calling that "Scott's Law". One of the questions last night was: "How do you manage to survive on no wages or small wages while your startup is getting off the ground?" It's a great question and honestly, it's probably one of the aspects of startups we don't talk about enough. There is no getting around it. At a certain point in your startup journey, you will need to go without a wage for some time. It may last months, but for some founders it can stretch into years. This is hard to achieve when you don't have a big bank balance. How do people solve for this? 1. High-paying partner. This is more common than you think, and many founders have a partner at home whose job pays the bills. 2. Take consulting gigs as you build. This is effectively how the Culture Amp founders bootstrapped their company for the first two years. Every few months, one of them would head back to corporate land and consult to reset the bank balance. Once they had enough cash, they would jump back into the startup. Rinse and repeat. 3. Stay in your full-time job until the startup can afford to pay you. This is possible, but really hard to do. This approach means your startup will take a considerable amount of time to build and even longer to gain traction. 4. Live at home with your parents and minimise your expenses. Only possible for the under-25s. 5. Spend a few decades in corporate land first and build up a nest egg via wages and property investments. 6. Win Tattslotto. Whatever you decide is right for you, it will require a financial sacrifice to make your startup a reality. It is one of many hurdles in entrepreneurship that mean only the truly committed end up taking the leap. Because often, it doesn't make financial sense to do it. But we do it anyway. 🤘 #letsgo

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